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Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-agent Systems
[chapter]
2005
Lecture Notes in Computer Science
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In this short paper we investigate the problem of learning to coordinate with heterogeneous agents. ...
This makes ef fective coordination particularly dif ficult to learn, especially in the absence of learning agent standards. ...
Conclusions and Outlook We have presented a proof of concept study of the learning of coordination for heterogeneous multi-agent systems. ...
doi:10.1007/978-3-540-32274-0_8
fatcat:k2wfnkvhfncahmjierb4re3qb4
Heterogeneous Agent Cooperative Planning Based on Q-Learning
2021
Journal of clean energy technologies
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In this paper, we present a model to achieve the collaboration of heterogeneous agent in the open-dynamic environment. ...
Heterogeneous rescue agent is used to assist agent in the scene. ...
Research on agent collaboration based on reinforcement Learning includes the multi-crawler system of the Internet [12] , research on Q-learning algorithm's multi-agent planning [13] and multi-agent ...
doi:10.7763/ijcte.2021.v13.1284
fatcat:uevmze3syzd5ro3saxvwfobjhi
Multi-Robot Cooperation Strategy in Game Environment Using Deep Reinforcement Learning
2018
2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
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Furthermore, we conducted experiments on the actual multi-robot system platform and demonstrated the feasibility of multi-agent cooperation strategy in practical multi-robot system based on deep reinforcement ...
The research progress of multi-agent decision-making strategies in the game environment based on deep reinforcement learning provides a solution for solving the problems faced by multi-robot systems. ...
In the second part of the paper, we review the multi-agent cooperation strategy research in the game environment based on deep reinforcement learning and its application on multi-robot systems. ...
doi:10.1109/robio.2018.8665165
dblp:conf/robio/ZhangLH18
fatcat:z6dsgnkvsbgvtcfcmrqqyci72i
Cooperative Multi-Agent Learning: The State of the Art
2005
Autonomous Agents and Multi-Agent Systems
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In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, complex systems, agent modeling, and robotics ...
Cooperative multi-agent systems are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. ...
Along with multi-agent systems in general, multi-agent learning is at the cusp of a major growth spurt. ...
doi:10.1007/s10458-005-2631-2
fatcat:u3xlftotajfitdtfmvbmggwgbi
Multi-Robot Information Fusion and Coordination Based on Agent
[chapter]
2011
Multi-Robot Systems, Trends and Development
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Multi-agent coordination based on reinforcement learning In this section, the multi-agent coordination based on distributed reinforcement learning is proposed, which is shown in Figure 11 . ...
In this section, a multi-agent coordination based on distributed reinforcement learning is proposed. ...
Multi-robot Information Fusion and Coordination Based on Agent 365 In member level, team games focus on the cooperation of member agents. ...
doi:10.5772/13029
fatcat:yg3uyhyb5vc7fb3jq7tzboyoh4
Transfer Learning Method Using Ontology for Heterogeneous Multi-agent Reinforcement Learning
2014
International Journal of Advanced Computer Science and Applications
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In MARS, autonomous agents obtain behavior autonomously through multi-agent reinforcement learning and the transfer learning method enables the reuse of the knowledge of other robots' behavior, such as ...
A multiagent robot system (MARS) that utilizes reinforcement learning and a transfer learning method has recently been studied in realworld situations. ...
ACKNOWLEDGMENT This work was partially supported by the Research Institute for Science and Technology of Tokyo Denki University Grant Number Q14J-01 Japan. ...
doi:10.14569/ijacsa.2014.051022
fatcat:iavbukidujayzp3q33izppabr4
Multi-robot concurrent learning of cooperative behaviours for the tracking of multiple moving targets
2006
International Journal of Vehicle Autonomous Systems
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Reinforcement learning has been extensively studied and applied for generating cooperative behaviours in multi-robot systems. ...
Furthermore, to address the problems in concurrent learning, we propose a distributed learning control algorithm to coordinate the concurrent learning processes. ...
In other words, for some cooperative multi-robot systems, the 'optimisation' lies in the relationship among robots. ...
doi:10.1504/ijvas.2006.012207
fatcat:6rrx7r5fh5gghdpphj2e73fbbe
Reinforcement learning of cooperative behaviors for multi-robot tracking of multiple moving targets
2005
2005 IEEE/RSJ International Conference on Intelligent Robots and Systems
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Index Terms-Reinforcement learning; concurrent learning; behavior based control; multi-robot cooperation. ...
In addition, to address the problems in concurrent learning, a distributed learning control algorithm is proposed to coordinate concurrent learning processes. ...
In last two decades, reinforcement learning has been extensively studied for multi-robot concurrent learning of cooperative behaviors. ...
doi:10.1109/iros.2005.1545146
dblp:conf/iros/LiuAS05
fatcat:sosemm7k2fc2beuwbxkzahkgkm
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging
[article]
2021
arXiv
pre-print
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Cooperative multi-agent reinforcement learning (MARL) has achieved significant results, most notably by leveraging the representation learning abilities of deep neural networks. ...
After explaining our MARL algorithm, hammer, and where it would be most applicable, we implement it in the cooperative navigation and multi-agent walker domains. ...
has taken place in the Intelligent Robot Learning (IRL) Lab at the University of Alberta, which is supported in part by research grants from the Alberta Machine Intelligence Institute (Amii), CIFAR, and ...
arXiv:2102.00824v1
fatcat:u3deetdxwvh6vffrqvueqto2xa
COOPERATIVE MULTI-AGENT LEARNING IN SOCIAL NETWORKS: A REVIEW
2018
International Journal of Advanced Research in Computer Science
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Coordination in cooperative multi-agent systems is one of the important issues in multi-agent learning and has been broadly studies in the literature. ...
In this work, we look over the multi-agent coordination problems in cooperative environments under the networked multi-agent learning framework using some social network structures and will try to improve ...
They studied (a simple form of reinforcement learning) Q-learning in cooperative multi-agent systems under the two perspectives, the first one focused on the influence of that game structure including ...
doi:10.26483/ijarcs.v9i2.5818
fatcat:enbuczj4ffh5rgp7ksrxkp6dbq
Reinforcement Learning in Dynamic Task Scheduling: A Review
2020
SN Computer Science
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Especially in real-world dynamic systems where multiple agents involve in scheduling various dynamic tasks is a challenging issue. ...
The paper addresses the results of the study by means of the state-of-theart on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques. ...
Compliance with ethical Standards Conflicts of Interest/Competing Interests The authors declare that there are no conflicts of interest regarding the publication of this article. ...
doi:10.1007/s42979-020-00326-5
fatcat:egp6vgpetbcwdasm45vunmo3n4
Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research
2011
Paladyn: Journal of Behavioral Robotics
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In this paper, we identify the main issues which offer opportunities to develop innovative solutions towards fully-scalable cooperative multi-agent systems. ...
the exponential state space growth, coordination issues, and the propagation of rewards among agents. ...
Cooperative Multi-Agent Reinforcement Algorithms In fully-cooperative systems, agents should coordinate to achieve the team goal and most authors consider a unique shared reward signal. ...
doi:10.2478/s13230-011-0017-5
fatcat:blzr2yqkbrdyfbkvolwyb2koge
Using distributed w-learning for multi-policy optimization in decentralized autonomic systems
2009
Proceedings of the 6th international conference on Autonomic computing - ICAC '09
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Distributed W-Learning (DWL) is a reinforcement learningbased algorithm for multi-policy optimization in agent-based systems. ...
In this poster we propose the use of DWL for decentralized multi-policy optimization in autonomic systems. ...
The authors would like to thank thank Fabian Bustamante for his feedback on the previous draft of this paper, As'ad Salkham for his implementation of the RL libraries used as the basis for our DWL implementation ...
doi:10.1145/1555228.1555247
dblp:conf/icac/DusparicC09
fatcat:qdiypkaegba6dj3anj7g6nh3za
A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions
[chapter]
2018
Applications of Mobile Robots [Working Title]
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Research in the area of cooperative multi-agent robot systems has received wide attention among researchers in recent years. ...
Therefore, this paper reviewed various selected literatures primarily from recent conference proceedings and journals related to cooperation and coordination of multi-agent robot systems (MARS). ...
Conflicts of interest Some works in this paper are based on study review from selected journals and proceedings regarding the cooperative multi-agent robot systems. ...
doi:10.5772/intechopen.79337
fatcat:ob2kmrbzcrekfcch7lby7l4wwy
Multiple Mobile Robot Systems
[chapter]
2008
Springer Handbook of Robotics
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techniques in Chapter 9) and in multi-agent systems [92] , much less work has been done in the area of multi-robot learning, although the topic is gaining increased interest. ...
Learning Multi-robot learning is the problem of learning new cooperative behaviors, or learning in the presence of other robots. ...
doi:10.1007/978-3-540-30301-5_41
fatcat:v3yo5joepja2lketepmahupkfm
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